AI Threat Alert
Paper 2603.03332v2
Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations
Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the robustness of this approach to corruptions in intermediate reasoning
medium relevance
survey
Paper 2601.13300v1
OI-Bench: An Option Injection Benchmark for Evaluating LLM Susceptibility to Directive Interference
signals such as social cues, framing, and instructions. In this work, we introduce option injection, a benchmarking approach that augments the multiple-choice question answering (MCQA) interface with an additional
high relevance
benchmark
Paper 2511.03675v1
Whisper Leak: a side-channel attack on Large Language Models
paramount. This paper introduces Whisper Leak, a side-channel attack that infers user prompt topics from encrypted LLM traffic by analyzing packet size and timing patterns in streaming responses. Despite
high relevance
attack
Paper 2510.00490v1
Has the Two-Decade-Old Prophecy Come True? Artificial Bad Intelligence Triggered by Merely a Single-Bit Flip in Large Language Models
Recently, Bit-Flip Attack (BFA) has garnered widespread attention for
medium relevance
attack
Previous
Page 15 of 15